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A Workflow Scheduling Method Based on the Combination of Tunicate Swarm Algorithm and Highest Response Ratio Next Scheduling

Yujie Tian, Ming Zhu, Jing Liu, Cong Liu, Ziyang Zhang

Research output: Contribution to journal › Article › peer-review

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Abstract

Workflow scheduling is critical for efficient cloud resource management. This paper proposes Tunicate Swarm-Highest Response Ratio Next, a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with the Highest Response Ratio Next policy. The Tunicate Swarm Algorithm generates a cost-minimizing task-to-VM mapping scheme, while the Highest Response Ratio Next dynamically dispatches tasks in the ready queue with the highest-priority. Experimental results demonstrate that the Tunicate Swarm-Highest Response Ratio Next reduces costs by up to 94.8% compared to meta-heuristic baselines. It also achieves competitive cost efficiency vs. a learning-based method while offering superior operational simplicity and efficiency, establishing it as a highly practical solution for dynamic cloud environments.
Original languageEnglish
Article number84
Number of pages14
JournalComputers, Materials & Continua
Volume87
Issue number2
DOIs
Publication statusPublished - 12 Mar 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Workflow scheduling
  • cloud computing
  • tunicate swarm algorithm
  • highest response ratio next scheduling

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